Enhancing Adaptability of Restoration Strategy for Distribution Network: A Meta-Based Graph Reinforcement Learning Approach

With the advancement of artificial intelligence, deep reinforcement learning (DRL) is emerging as an effective solution for the distribution system service restoration. However, traditional DRL approaches are typically tailored for training agents in specific scenarios, limiting their ability to adapt rapidly to new environments. Furthermore, the spatial characteristics of the distribution network are largely ignored during the training, constraining the state perception capabilities of agents. To address these issues, this article proposes a meta-based graph reinforcement learning approach that combines graph learning, meta-learning, and reinforcement learning for the learning of service restoration strategies in the distribution network. The agent trained by such an approach possesses the feature perception capability of graph learning, allowing it to acquire deeper service restoration strategies from latent graph features. Moreover, the agent also has the fast adaptation ability of meta-learning, enabling it to quickly adapt to new restoration scenarios. Experimental results demonstrate that the proposed approach outperforms existing results of both the specialized and generalized strategies.